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检索条件"主题词=Graph Algorithm"

共 518 条记录，以下是81-90 订阅

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graphLib: A Parallel graph Mining Library for Joint Cloud Computing

GraphLib: A Parallel Graph Mining Library for Joint Cloud Co...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Zhang, Kai Fang, Yange Zheng, Yingying Zeng, Hongbin Xu, Lijie Wang, Wei Chinese Acad Sci State Key Lab Comp Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728169811

graph algorithms are widely applied in social networks, computational biology, Internet security and a broad range of complexity science. Although there are many state-of-the-art graph frameworks, few frameworks support parallel graph mining in joint cloud computing environment. In this paper, we propose graphLib, a parallel graph mining library, based on a BSP (Bulk Synchronous Parallel) service over joint cloud computing which was proposed in our prior work. We first summarize the features of commonly-used graph mining algorithms, and present our approaches for parallelizing typical graph mining algorithms. graphLib includes 17 parallel graph mining algorithms that can be used in 3 scenarios. We evaluate the performance of 4 typical parallel graph algorithms in graphLib on three real-world datasets. Our parallelized algorithms can achieve sub-linear scalability.

关键词： graph algorithm BSP JointCloud

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Distributed Triangle Counting algorithms in Simple graph Stream 25

Distributed Triangle Counting Algorithms in Simple Graph Str...

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25th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Yu, Mengdi Song, Chao Gu, Jiqing Liu, Ming Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Big Data Res Ctr Chengdu Peoples R China

ISBN: (纸本)9781728125831

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, one-pass streaming algorithm for counting global and local triangles has been widely studied, and most researches focus on the single-machine streaming algorithm in a 'offline+batch processing' mode. However, researches on distributed online algorithm on multiple machines are still in its infancy, and this stage has not been thoroughly studied. In this paper, we investigate the triangle counting problem in large-scale simple undirected graphs whose edges arrive as a stream. We propose two distributed online streaming algorithms to estimate the global number of triangles, which are based on the current best performance sampling-based streaming algorithm. We mainly realize the reasonable partition of the graph stream, so that each worker independently estimates the number of triangles in a subgraph of the graph stream. Experimental results show that our algorithms reduce the estimation error and are several times more accurate than state-of-the-art streaming algorithms.

关键词： Approximate algorithm distributed streaming algorithm graph algorithm graph stream triangle counting algorithm

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DisNet: A Framework for Distributed graph Computation

DisNet: A Framework for Distributed Graph Computation

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International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

作者： Lichtenwalter, Ryan Chawla, Nitesh V. Univ Notre Dame Dept Comp Sci Notre Dame IN 46556 USA

ISBN: (纸本)9780769543758

With the rise of network science as an exciting interdisciplinary research topic, efficient graph algorithms are in high demand. Problematically, many such algorithms measuring important properties of networks have asymptotic lower bounds that are quadratic, cubic, or higher in the number of vertices. For analysis of social networks, transportation networks, communication networks, and a host of others, computation is intractable. In these networks computation in serial fashion requires years or even decades. Fortunately, these same computational problems are often naturally parallel. We present here the design and implementation of a master-worker framework for easily computing such results in these circumstances. The user needs only to supply two small fragments of code describing the fundamental kernel of the computation. The framework automatically divides and distributes the workload and manages completion using an arbitrary number of heterogeneous computational resources. In practice, we have used thousands of machines and observed commensurate speedups. Writing only 31 lines of standard C++ code, we computed betweenness centrality on a network of 4.7M nodes in 25 hours.

关键词： algorithm design and analysis Approximation algorithms C++ language Data structures DisNet Instruction sets Libraries Parallel processing Writing asymptotic lower bounds betweenness centrality communication network analysis distributed computing distributed graph computation graph algorithm graph algorithms graph theory heterogeneous computational resources map-reduce master-worker framework network science network theory (graphs) parallel algorithms parallel processing social network analysis standard C++ code transportation network analysis

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Sigcon: Simplifying a graph Based on Degree Correlation and Clustering Coefficient 19

Sigcon: Simplifying a Graph Based on Degree Correlation and ...

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19th IEEE International Conference on High Performance Computing and Communications (HPCC) / 15th IEEE International Conference on Smart City (SmartCity) / 3rd IEEE International Conference on Data Science and Systems (DSS)

作者： Jung, Hojin Kim, Songkuk Yonsei Univ Yonsei Inst Convergence Technol Sch Integrated Technol Incheon South Korea

ISBN: (纸本)9781538625880

Depicting a complex system like social networks as a graph helps understand its structure and relation. As advances in technology increase the amount of data, simplifying a large-scale graph has attracted interests. Simplification reduces the size of a graph while preserving its important properties. In this paper, we propose the summarization algorithm to simplify a graph focusing on degree correlation and clustering coefficient. The degree correlation is a measure to assess the influence of each vertex and their connections. The clustering coefficient estimates latent connections between two distinct vertices. To this end, we first separate a graph into communities. Looking at groups instead of a graph itself allows us to extract important vertices and edges more easily. We then categorize communities into four cases and simplify them in different ways to preserve the innate characteristics. The quantitative and qualitative evaluations demonstrate how effectively our algorithm serves the goal. Overall, our contributions are as follows: (a) unique pattern: We found that hubs are connected indirectly via low-degree vertices. Sigcon preserves these vertices during simplification. (b) efficient algorithm: Sigcon identifies influential vertices and edges to simplify a graph on the basis of degree correlation and clustering coefficient connecting vertices effectively.

关键词： graph algorithm Simplification Degree Correlation Clustering Coefficient

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Scalable Hybrid Implementation of graph Coloring using MPI and OpenMP

Scalable Hybrid Implementation of Graph Coloring using MPI a...

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Sariyuece, Ahmet Erdem Saule, Erik Catalyuerek, Uemit V. Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA

ISBN: (纸本)9780769546766

graph coloring algorithms are commonly used in large scientific parallel computing either for identifying parallelism or as a tool to reduce computation, such as compressing Hessian matrices. Large scientific computations are nowadays either run on commodity clusters or on large computing platforms. In both cases, the current target platform is hierarchical with distributed memory at the node level and shared memory at the processor level. In this paper, we present a novel hybrid graph coloring algorithm and discuss how to obtain the best performance on such systems from algorithmic, system and engineering perspectives.

关键词： graph algorithm graph coloring Distributed Memory Shared Memory Hybrid programming

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Ontology Reshaping for Knowledge graph Construction: Applied on Bosch Welding Case 21st

Ontology Reshaping for Knowledge Graph Construction: Applied...

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21st International Semantic Web Conference (ISWC)

作者： Zhou, Dongzhuoran Zhou, Baifan Zheng, Zhuoxun Soylu, Ahmet Cheng, Gong Jimenez-Ruiz, Ernesto Kostylev, Egor V. Kharlamov, Evgeny Bosch Ctr Artificial Intelligence Renningen Germany Univ Oslo Dept Informat Oslo Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China City Univ London Dept Comp Sci London England

ISBN: (纸本)9783031194320;9783031194337

Automatic knowledge graph (KG) construction is widely used in industry for data integration and access, and there are several approaches to enable (semi-)automatic construction of knowledge graphs. One important approach is to map the raw data to a given knowledge graph schema, often a domain ontology, and construct the entities and properties according to the ontology. However, the existing approaches to construct knowledge graphs are not always efficient enough and the resulting knowledge graphs are not sufficiently application-oriented and user-friendly. The challenge arises from the trade-off: the domain ontology should be knowledge-oriented, to reflect the general domain knowledge rather than data particularities;while a knowledge graph schema should be data-oriented, to cover all data features. If the former is directly used as the knowledge graph schema, this can cause issues like blank nodes created due to classes unmapped to data and deep knowledge graph structures. To this end, we propose a system for ontology reshaping, which generates knowledge graph schemata that fully cover the data while also covers domain knowledge well. We evaluated our approach extensively with a user study and three real manufacturing datasets from Bosch against four baselines, showing promising results.

关键词： Semantic data integration Knowledge graph Ontology reshaping graph algorithm Automatic knowledge graph construction

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graph-BASED FOREGROUND EXTRACTION IN EXTENDED COLOR SPACE

GRAPH-BASED FOREGROUND EXTRACTION IN EXTENDED COLOR SPACE

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16th IEEE International Conference on Image Processing

作者： Kim, Hansung Hilton, Adrian Univ Surrey Ctr Vis Speech & Signal Proc Surrey GU2 7XH England

ISBN: (纸本)9781424456536

We propose a region-based method to extract semantic foreground regions from color video sequences with static backgrounds. First, we introduce a new distance measure for background subtraction which is robust against shadows. Then the foreground region is extracted with a graph-based region segmentation method considering background difference and spatial homogeneity. For efficient computation, the graph structure is optimized by the minimum spanning tree before segmentation. The main contribution is that the proposed algorithm improves on conventional approaches especially in strong shadow regions and does not require manual initialization. We have verified through experiments and comparison to state of the art methods that the proposed algorithm works well with various cameras and environment.

关键词： Background subtraction graph algorithm Minimum spanning tree

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HAGP: A Hub-centric Asynchronous graph Processing Framework for Scale-free graph 15

HAGP: A Hub-centric Asynchronous Graph Processing Framework ...

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2015 15th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGrid 2015)

作者： Gao, Tao Lu, Yutong Zhang, Baida Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479980062

graph structure which is often used to model the relationship between the data items has drawn more and more attention. The graph datasets from many important domains have the property called scale-free. In the scale-free graphs, there exist the hubs, which have much larger degree than the average value. The hubs may cause the problems of load imbalance, poor scalability and high communication overhead when the graphs are processed in the distributed memory systems. In this paper, we design an asynchronous graph processing framework targeted for distributed memory by considering the hubs as a separate part of the vertexes, which we call it the hub-centric idea. Specifically speaking, a hub-duplicate graph partitioning method is proposed to balance the workload and reduce the communication overhead. At the same time, an efficient asynchronous state synchronization method for the duplicates is also proposed. In addition, a priority scheduling strategy is applied to further reduce the communication overhead.

关键词： graph processing graph algorithm parallel algorithm distributed computing

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Scalable graph and mesh algorithms on distributed-memory systems

Scalable graph and mesh algorithms on distributed-memory sys...

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作者： Panitanarak, Thap PennState University Libraries

学位级别：Doctor of Philosophy

Big datasets are now becoming a standard quantity in large-scale data analysis; they involve social and information network, and scientific mesh computations. These datasets are commonly stored and processed across multiple machines due to limited capabilities (such as memory and CPU) of single machines. However, many available analysis tools are still lacking in terms of an ability to fully utilize existing distributed-memory architectures. As these datasets are usually processed and analyzed in the form of graphs or meshes, we propose scalable and efficient approaches for graph and mesh computations for distributed-memory systems in this dissertation. Although graph and mesh computations are closely related regarding their parallelization approaches, some of their unique characteristics still need to be addressed separately. Thus, we organize the dissertation into two parts. The first part is for distributed graph computations, and the second part is for distributed mesh *** the first part of the dissertation, we focus on graph computations. First, we study a problem of Single-Source Shortest Path (SSSP) by analyzing and evaluating three well-known SSSP algorithms, i.e, Dijkstra's, Bellman-Ford, and $\Delta$-stepping algorithms. We implement these algorithms to run on distributed-memory systems based on a bulk synchronous parallel model. Their performances are evaluated and compared. Next, we propose our SSSP algorithm by combining advantages of these SSSP algorithms and utilizing a two-dimensional (2D) graph layout for our graph data structures. Then, we extend our study of the 2D graph data structures and optimization approaches to other well-known graph algorithms including breadth-first search, approximate diameter, connected components, and PageRank on various real-world graphs. Our objective is to implement an efficient graph framework for distributed-memory systems that works efficiently for many graph algorithms on various graph types. Finally,

关键词： parallel computation graph algorithm distributed memory algorithm graph data structure linear solver mesh warping SSSP mesh partitioning

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Parallel Subgraph Listing in a Large-Scale graph 14

Parallel Subgraph Listing in a Large-Scale Graph

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ACM SIGMOD International Conference on Management of Data

作者： Shao, Yingxia Cui, Bin Chen, Lei Ma, Lin Yao, Junjie Xu, Ning Peking Univ Sch EECS Key Lab High Confidence Software Technol MOE Beijing Peoples R China HKUST Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781450323765

Subgraph listing is a fundamental operation to many graph and network analyses. The problem itself is computationally expensive and is well-studied in centralized processing algorithms. However, the centralized solutions cannot scale well to large graphs. Recently, several parallel approaches are introduced to handle the large graphs. Unfortunately, these parallel approaches still rely on the expensive join operations, thus cannot achieve high performance. In this paper, we design a novel parallel subgraph listing framework, named PSgL. The PSgL iteratively enumerates subgraph instances and solves the subgraph listing in a divide-and-conquer fashion. The framework completely relies on the graph traversal, and avoids the explicit join operation. Moreover, in order to improve its performance, we propose several solutions to balance the workload and reduce the size of intermediate results. Specially, we prove the problem of partial subgraph instance distribution for workload balance is NP-hard, and carefully design a set of heuristic strategies. To further reduce the enormous intermediate results, we introduce three independent mechanisms, which are automorphism breaking of the pattern graph, initial pattern vertex selection based on a cost model, and a pruning method based on a light-weight index. We have implemented the prototype of PSgL, and run comprehensive experiments of various graph listing operations on diverse large graphs. The experiments clearly demonstrate that PSgL is robust and can achieve performance gain over the state-of-the-art solutions up to 90%.

关键词： graph graph algorithm Subgraph Listing graph Enumeration

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